{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_code</th>\n",
       "      <th>target</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
       "      <th>var_6</th>\n",
       "      <th>var_7</th>\n",
       "      <th>...</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
       "      <th>var_194</th>\n",
       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
       "      <th>var_197</th>\n",
       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.9255</td>\n",
       "      <td>-6.7863</td>\n",
       "      <td>11.9081</td>\n",
       "      <td>5.0930</td>\n",
       "      <td>11.4607</td>\n",
       "      <td>-9.2834</td>\n",
       "      <td>5.1187</td>\n",
       "      <td>18.6266</td>\n",
       "      <td>...</td>\n",
       "      <td>4.4354</td>\n",
       "      <td>3.9642</td>\n",
       "      <td>3.1364</td>\n",
       "      <td>1.6910</td>\n",
       "      <td>18.5227</td>\n",
       "      <td>-2.3978</td>\n",
       "      <td>7.8784</td>\n",
       "      <td>8.5635</td>\n",
       "      <td>12.7803</td>\n",
       "      <td>-1.0914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.5006</td>\n",
       "      <td>-4.1473</td>\n",
       "      <td>13.8588</td>\n",
       "      <td>5.3890</td>\n",
       "      <td>12.3622</td>\n",
       "      <td>7.0433</td>\n",
       "      <td>5.6208</td>\n",
       "      <td>16.5338</td>\n",
       "      <td>...</td>\n",
       "      <td>7.6421</td>\n",
       "      <td>7.7214</td>\n",
       "      <td>2.5837</td>\n",
       "      <td>10.9516</td>\n",
       "      <td>15.4305</td>\n",
       "      <td>2.0339</td>\n",
       "      <td>8.1267</td>\n",
       "      <td>8.7889</td>\n",
       "      <td>18.3560</td>\n",
       "      <td>1.9518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6093</td>\n",
       "      <td>-2.7457</td>\n",
       "      <td>12.0805</td>\n",
       "      <td>7.8928</td>\n",
       "      <td>10.5825</td>\n",
       "      <td>-9.0837</td>\n",
       "      <td>6.9427</td>\n",
       "      <td>14.6155</td>\n",
       "      <td>...</td>\n",
       "      <td>2.9057</td>\n",
       "      <td>9.7905</td>\n",
       "      <td>1.6704</td>\n",
       "      <td>1.6858</td>\n",
       "      <td>21.6042</td>\n",
       "      <td>3.1417</td>\n",
       "      <td>-6.5213</td>\n",
       "      <td>8.2675</td>\n",
       "      <td>14.7222</td>\n",
       "      <td>0.3965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_3</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0604</td>\n",
       "      <td>-2.1518</td>\n",
       "      <td>8.9522</td>\n",
       "      <td>7.1957</td>\n",
       "      <td>12.5846</td>\n",
       "      <td>-1.8361</td>\n",
       "      <td>5.8428</td>\n",
       "      <td>14.9250</td>\n",
       "      <td>...</td>\n",
       "      <td>4.4666</td>\n",
       "      <td>4.7433</td>\n",
       "      <td>0.7178</td>\n",
       "      <td>1.4214</td>\n",
       "      <td>23.0347</td>\n",
       "      <td>-1.2706</td>\n",
       "      <td>-2.9275</td>\n",
       "      <td>10.2922</td>\n",
       "      <td>17.9697</td>\n",
       "      <td>-8.9996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_4</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8369</td>\n",
       "      <td>-1.4834</td>\n",
       "      <td>12.8746</td>\n",
       "      <td>6.6375</td>\n",
       "      <td>12.2772</td>\n",
       "      <td>2.4486</td>\n",
       "      <td>5.9405</td>\n",
       "      <td>19.2514</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.4905</td>\n",
       "      <td>9.5214</td>\n",
       "      <td>-0.1508</td>\n",
       "      <td>9.1942</td>\n",
       "      <td>13.2876</td>\n",
       "      <td>-1.5121</td>\n",
       "      <td>3.9267</td>\n",
       "      <td>9.5031</td>\n",
       "      <td>17.9974</td>\n",
       "      <td>-8.8104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 202 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID_code  target    var_0   var_1    var_2   var_3    var_4   var_5   var_6  \\\n",
       "0  train_0       0   8.9255 -6.7863  11.9081  5.0930  11.4607 -9.2834  5.1187   \n",
       "1  train_1       0  11.5006 -4.1473  13.8588  5.3890  12.3622  7.0433  5.6208   \n",
       "2  train_2       0   8.6093 -2.7457  12.0805  7.8928  10.5825 -9.0837  6.9427   \n",
       "3  train_3       0  11.0604 -2.1518   8.9522  7.1957  12.5846 -1.8361  5.8428   \n",
       "4  train_4       0   9.8369 -1.4834  12.8746  6.6375  12.2772  2.4486  5.9405   \n",
       "\n",
       "     var_7   ...     var_190  var_191  var_192  var_193  var_194  var_195  \\\n",
       "0  18.6266   ...      4.4354   3.9642   3.1364   1.6910  18.5227  -2.3978   \n",
       "1  16.5338   ...      7.6421   7.7214   2.5837  10.9516  15.4305   2.0339   \n",
       "2  14.6155   ...      2.9057   9.7905   1.6704   1.6858  21.6042   3.1417   \n",
       "3  14.9250   ...      4.4666   4.7433   0.7178   1.4214  23.0347  -1.2706   \n",
       "4  19.2514   ...     -1.4905   9.5214  -0.1508   9.1942  13.2876  -1.5121   \n",
       "\n",
       "   var_196  var_197  var_198  var_199  \n",
       "0   7.8784   8.5635  12.7803  -1.0914  \n",
       "1   8.1267   8.7889  18.3560   1.9518  \n",
       "2  -6.5213   8.2675  14.7222   0.3965  \n",
       "3  -2.9275  10.2922  17.9697  -8.9996  \n",
       "4   3.9267   9.5031  17.9974  -8.8104  \n",
       "\n",
       "[5 rows x 202 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
       "      <th>var_6</th>\n",
       "      <th>var_7</th>\n",
       "      <th>var_8</th>\n",
       "      <th>...</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
       "      <th>var_194</th>\n",
       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
       "      <th>var_197</th>\n",
       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.100490</td>\n",
       "      <td>10.679914</td>\n",
       "      <td>-1.627622</td>\n",
       "      <td>10.715192</td>\n",
       "      <td>6.796529</td>\n",
       "      <td>11.078333</td>\n",
       "      <td>-5.065317</td>\n",
       "      <td>5.408949</td>\n",
       "      <td>16.545850</td>\n",
       "      <td>0.284162</td>\n",
       "      <td>...</td>\n",
       "      <td>3.234440</td>\n",
       "      <td>7.438408</td>\n",
       "      <td>1.927839</td>\n",
       "      <td>3.331774</td>\n",
       "      <td>17.993784</td>\n",
       "      <td>-0.142088</td>\n",
       "      <td>2.303335</td>\n",
       "      <td>8.908158</td>\n",
       "      <td>15.870720</td>\n",
       "      <td>-3.326537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.300653</td>\n",
       "      <td>3.040051</td>\n",
       "      <td>4.050044</td>\n",
       "      <td>2.640894</td>\n",
       "      <td>2.043319</td>\n",
       "      <td>1.623150</td>\n",
       "      <td>7.863267</td>\n",
       "      <td>0.866607</td>\n",
       "      <td>3.418076</td>\n",
       "      <td>3.332634</td>\n",
       "      <td>...</td>\n",
       "      <td>4.559922</td>\n",
       "      <td>3.023272</td>\n",
       "      <td>1.478423</td>\n",
       "      <td>3.992030</td>\n",
       "      <td>3.135162</td>\n",
       "      <td>1.429372</td>\n",
       "      <td>5.454369</td>\n",
       "      <td>0.921625</td>\n",
       "      <td>3.010945</td>\n",
       "      <td>10.438015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.408400</td>\n",
       "      <td>-15.043400</td>\n",
       "      <td>2.117100</td>\n",
       "      <td>-0.040200</td>\n",
       "      <td>5.074800</td>\n",
       "      <td>-32.562600</td>\n",
       "      <td>2.347300</td>\n",
       "      <td>5.349700</td>\n",
       "      <td>-10.505500</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.093300</td>\n",
       "      <td>-2.691700</td>\n",
       "      <td>-3.814500</td>\n",
       "      <td>-11.783400</td>\n",
       "      <td>8.694400</td>\n",
       "      <td>-5.261000</td>\n",
       "      <td>-14.209600</td>\n",
       "      <td>5.960600</td>\n",
       "      <td>6.299300</td>\n",
       "      <td>-38.852800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.453850</td>\n",
       "      <td>-4.740025</td>\n",
       "      <td>8.722475</td>\n",
       "      <td>5.254075</td>\n",
       "      <td>9.883175</td>\n",
       "      <td>-11.200350</td>\n",
       "      <td>4.767700</td>\n",
       "      <td>13.943800</td>\n",
       "      <td>-2.317800</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058825</td>\n",
       "      <td>5.157400</td>\n",
       "      <td>0.889775</td>\n",
       "      <td>0.584600</td>\n",
       "      <td>15.629800</td>\n",
       "      <td>-1.170700</td>\n",
       "      <td>-1.946925</td>\n",
       "      <td>8.252800</td>\n",
       "      <td>13.829700</td>\n",
       "      <td>-11.208475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.524750</td>\n",
       "      <td>-1.608050</td>\n",
       "      <td>10.580000</td>\n",
       "      <td>6.825000</td>\n",
       "      <td>11.108250</td>\n",
       "      <td>-4.833150</td>\n",
       "      <td>5.385100</td>\n",
       "      <td>16.456800</td>\n",
       "      <td>0.393700</td>\n",
       "      <td>...</td>\n",
       "      <td>3.203600</td>\n",
       "      <td>7.347750</td>\n",
       "      <td>1.901300</td>\n",
       "      <td>3.396350</td>\n",
       "      <td>17.957950</td>\n",
       "      <td>-0.172700</td>\n",
       "      <td>2.408900</td>\n",
       "      <td>8.888200</td>\n",
       "      <td>15.934050</td>\n",
       "      <td>-2.819550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.758200</td>\n",
       "      <td>1.358625</td>\n",
       "      <td>12.516700</td>\n",
       "      <td>8.324100</td>\n",
       "      <td>12.261125</td>\n",
       "      <td>0.924800</td>\n",
       "      <td>6.003000</td>\n",
       "      <td>19.102900</td>\n",
       "      <td>2.937900</td>\n",
       "      <td>...</td>\n",
       "      <td>6.406200</td>\n",
       "      <td>9.512525</td>\n",
       "      <td>2.949500</td>\n",
       "      <td>6.205800</td>\n",
       "      <td>20.396525</td>\n",
       "      <td>0.829600</td>\n",
       "      <td>6.556725</td>\n",
       "      <td>9.593300</td>\n",
       "      <td>18.064725</td>\n",
       "      <td>4.836800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.315000</td>\n",
       "      <td>10.376800</td>\n",
       "      <td>19.353000</td>\n",
       "      <td>13.188300</td>\n",
       "      <td>16.671400</td>\n",
       "      <td>17.251600</td>\n",
       "      <td>8.447700</td>\n",
       "      <td>27.691800</td>\n",
       "      <td>10.151300</td>\n",
       "      <td>...</td>\n",
       "      <td>18.440900</td>\n",
       "      <td>16.716500</td>\n",
       "      <td>8.402400</td>\n",
       "      <td>18.281800</td>\n",
       "      <td>27.928800</td>\n",
       "      <td>4.272900</td>\n",
       "      <td>18.321500</td>\n",
       "      <td>12.000400</td>\n",
       "      <td>26.079100</td>\n",
       "      <td>28.500700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              target          var_0          var_1          var_2  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.100490      10.679914      -1.627622      10.715192   \n",
       "std         0.300653       3.040051       4.050044       2.640894   \n",
       "min         0.000000       0.408400     -15.043400       2.117100   \n",
       "25%         0.000000       8.453850      -4.740025       8.722475   \n",
       "50%         0.000000      10.524750      -1.608050      10.580000   \n",
       "75%         0.000000      12.758200       1.358625      12.516700   \n",
       "max         1.000000      20.315000      10.376800      19.353000   \n",
       "\n",
       "               var_3          var_4          var_5          var_6  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        6.796529      11.078333      -5.065317       5.408949   \n",
       "std         2.043319       1.623150       7.863267       0.866607   \n",
       "min        -0.040200       5.074800     -32.562600       2.347300   \n",
       "25%         5.254075       9.883175     -11.200350       4.767700   \n",
       "50%         6.825000      11.108250      -4.833150       5.385100   \n",
       "75%         8.324100      12.261125       0.924800       6.003000   \n",
       "max        13.188300      16.671400      17.251600       8.447700   \n",
       "\n",
       "               var_7          var_8      ...              var_190  \\\n",
       "count  200000.000000  200000.000000      ...        200000.000000   \n",
       "mean       16.545850       0.284162      ...             3.234440   \n",
       "std         3.418076       3.332634      ...             4.559922   \n",
       "min         5.349700     -10.505500      ...           -14.093300   \n",
       "25%        13.943800      -2.317800      ...            -0.058825   \n",
       "50%        16.456800       0.393700      ...             3.203600   \n",
       "75%        19.102900       2.937900      ...             6.406200   \n",
       "max        27.691800      10.151300      ...            18.440900   \n",
       "\n",
       "             var_191        var_192        var_193        var_194  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        7.438408       1.927839       3.331774      17.993784   \n",
       "std         3.023272       1.478423       3.992030       3.135162   \n",
       "min        -2.691700      -3.814500     -11.783400       8.694400   \n",
       "25%         5.157400       0.889775       0.584600      15.629800   \n",
       "50%         7.347750       1.901300       3.396350      17.957950   \n",
       "75%         9.512525       2.949500       6.205800      20.396525   \n",
       "max        16.716500       8.402400      18.281800      27.928800   \n",
       "\n",
       "             var_195        var_196        var_197        var_198  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -0.142088       2.303335       8.908158      15.870720   \n",
       "std         1.429372       5.454369       0.921625       3.010945   \n",
       "min        -5.261000     -14.209600       5.960600       6.299300   \n",
       "25%        -1.170700      -1.946925       8.252800      13.829700   \n",
       "50%        -0.172700       2.408900       8.888200      15.934050   \n",
       "75%         0.829600       6.556725       9.593300      18.064725   \n",
       "max         4.272900      18.321500      12.000400      26.079100   \n",
       "\n",
       "             var_199  \n",
       "count  200000.000000  \n",
       "mean       -3.326537  \n",
       "std        10.438015  \n",
       "min       -38.852800  \n",
       "25%       -11.208475  \n",
       "50%        -2.819550  \n",
       "75%         4.836800  \n",
       "max        28.500700  \n",
       "\n",
       "[8 rows x 201 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values in each feature\n",
      "ID_code: 0\n",
      "target: 0\n",
      "var_0: 0\n",
      "var_1: 0\n",
      "var_2: 0\n",
      "var_3: 0\n",
      "var_4: 0\n",
      "var_5: 0\n",
      "var_6: 0\n",
      "var_7: 0\n",
      "var_8: 0\n",
      "var_9: 0\n",
      "var_10: 0\n",
      "var_11: 0\n",
      "var_12: 0\n",
      "var_13: 0\n",
      "var_14: 0\n",
      "var_15: 0\n",
      "var_16: 0\n",
      "var_17: 0\n",
      "var_18: 0\n",
      "var_19: 0\n",
      "var_20: 0\n",
      "var_21: 0\n",
      "var_22: 0\n",
      "var_23: 0\n",
      "var_24: 0\n",
      "var_25: 0\n",
      "var_26: 0\n",
      "var_27: 0\n",
      "var_28: 0\n",
      "var_29: 0\n",
      "var_30: 0\n",
      "var_31: 0\n",
      "var_32: 0\n",
      "var_33: 0\n",
      "var_34: 0\n",
      "var_35: 0\n",
      "var_36: 0\n",
      "var_37: 0\n",
      "var_38: 0\n",
      "var_39: 0\n",
      "var_40: 0\n",
      "var_41: 0\n",
      "var_42: 0\n",
      "var_43: 0\n",
      "var_44: 0\n",
      "var_45: 0\n",
      "var_46: 0\n",
      "var_47: 0\n",
      "var_48: 0\n",
      "var_49: 0\n",
      "var_50: 0\n",
      "var_51: 0\n",
      "var_52: 0\n",
      "var_53: 0\n",
      "var_54: 0\n",
      "var_55: 0\n",
      "var_56: 0\n",
      "var_57: 0\n",
      "var_58: 0\n",
      "var_59: 0\n",
      "var_60: 0\n",
      "var_61: 0\n",
      "var_62: 0\n",
      "var_63: 0\n",
      "var_64: 0\n",
      "var_65: 0\n",
      "var_66: 0\n",
      "var_67: 0\n",
      "var_68: 0\n",
      "var_69: 0\n",
      "var_70: 0\n",
      "var_71: 0\n",
      "var_72: 0\n",
      "var_73: 0\n",
      "var_74: 0\n",
      "var_75: 0\n",
      "var_76: 0\n",
      "var_77: 0\n",
      "var_78: 0\n",
      "var_79: 0\n",
      "var_80: 0\n",
      "var_81: 0\n",
      "var_82: 0\n",
      "var_83: 0\n",
      "var_84: 0\n",
      "var_85: 0\n",
      "var_86: 0\n",
      "var_87: 0\n",
      "var_88: 0\n",
      "var_89: 0\n",
      "var_90: 0\n",
      "var_91: 0\n",
      "var_92: 0\n",
      "var_93: 0\n",
      "var_94: 0\n",
      "var_95: 0\n",
      "var_96: 0\n",
      "var_97: 0\n",
      "var_98: 0\n",
      "var_99: 0\n",
      "var_100: 0\n",
      "var_101: 0\n",
      "var_102: 0\n",
      "var_103: 0\n",
      "var_104: 0\n",
      "var_105: 0\n",
      "var_106: 0\n",
      "var_107: 0\n",
      "var_108: 0\n",
      "var_109: 0\n",
      "var_110: 0\n",
      "var_111: 0\n",
      "var_112: 0\n",
      "var_113: 0\n",
      "var_114: 0\n",
      "var_115: 0\n",
      "var_116: 0\n",
      "var_117: 0\n",
      "var_118: 0\n",
      "var_119: 0\n",
      "var_120: 0\n",
      "var_121: 0\n",
      "var_122: 0\n",
      "var_123: 0\n",
      "var_124: 0\n",
      "var_125: 0\n",
      "var_126: 0\n",
      "var_127: 0\n",
      "var_128: 0\n",
      "var_129: 0\n",
      "var_130: 0\n",
      "var_131: 0\n",
      "var_132: 0\n",
      "var_133: 0\n",
      "var_134: 0\n",
      "var_135: 0\n",
      "var_136: 0\n",
      "var_137: 0\n",
      "var_138: 0\n",
      "var_139: 0\n",
      "var_140: 0\n",
      "var_141: 0\n",
      "var_142: 0\n",
      "var_143: 0\n",
      "var_144: 0\n",
      "var_145: 0\n",
      "var_146: 0\n",
      "var_147: 0\n",
      "var_148: 0\n",
      "var_149: 0\n",
      "var_150: 0\n",
      "var_151: 0\n",
      "var_152: 0\n",
      "var_153: 0\n",
      "var_154: 0\n",
      "var_155: 0\n",
      "var_156: 0\n",
      "var_157: 0\n",
      "var_158: 0\n",
      "var_159: 0\n",
      "var_160: 0\n",
      "var_161: 0\n",
      "var_162: 0\n",
      "var_163: 0\n",
      "var_164: 0\n",
      "var_165: 0\n",
      "var_166: 0\n",
      "var_167: 0\n",
      "var_168: 0\n",
      "var_169: 0\n",
      "var_170: 0\n",
      "var_171: 0\n",
      "var_172: 0\n",
      "var_173: 0\n",
      "var_174: 0\n",
      "var_175: 0\n",
      "var_176: 0\n",
      "var_177: 0\n",
      "var_178: 0\n",
      "var_179: 0\n",
      "var_180: 0\n",
      "var_181: 0\n",
      "var_182: 0\n",
      "var_183: 0\n",
      "var_184: 0\n",
      "var_185: 0\n",
      "var_186: 0\n",
      "var_187: 0\n",
      "var_188: 0\n",
      "var_189: 0\n",
      "var_190: 0\n",
      "var_191: 0\n",
      "var_192: 0\n",
      "var_193: 0\n",
      "var_194: 0\n",
      "var_195: 0\n",
      "var_196: 0\n",
      "var_197: 0\n",
      "var_198: 0\n",
      "var_199: 0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "features=[k for k in train]\n",
    "print('Missing Values in each feature')\n",
    "for feat in features:\n",
    "    print('%s: %s'%(feat,train[feat].isnull().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200000 entries, 0 to 199999\n",
      "Data columns (total 202 columns):\n",
      "ID_code    200000 non-null object\n",
      "target     200000 non-null int64\n",
      "var_0      200000 non-null float64\n",
      "var_1      200000 non-null float64\n",
      "var_2      200000 non-null float64\n",
      "var_3      200000 non-null float64\n",
      "var_4      200000 non-null float64\n",
      "var_5      200000 non-null float64\n",
      "var_6      200000 non-null float64\n",
      "var_7      200000 non-null float64\n",
      "var_8      200000 non-null float64\n",
      "var_9      200000 non-null float64\n",
      "var_10     200000 non-null float64\n",
      "var_11     200000 non-null float64\n",
      "var_12     200000 non-null float64\n",
      "var_13     200000 non-null float64\n",
      "var_14     200000 non-null float64\n",
      "var_15     200000 non-null float64\n",
      "var_16     200000 non-null float64\n",
      "var_17     200000 non-null float64\n",
      "var_18     200000 non-null float64\n",
      "var_19     200000 non-null float64\n",
      "var_20     200000 non-null float64\n",
      "var_21     200000 non-null float64\n",
      "var_22     200000 non-null float64\n",
      "var_23     200000 non-null float64\n",
      "var_24     200000 non-null float64\n",
      "var_25     200000 non-null float64\n",
      "var_26     200000 non-null float64\n",
      "var_27     200000 non-null float64\n",
      "var_28     200000 non-null float64\n",
      "var_29     200000 non-null float64\n",
      "var_30     200000 non-null float64\n",
      "var_31     200000 non-null float64\n",
      "var_32     200000 non-null float64\n",
      "var_33     200000 non-null float64\n",
      "var_34     200000 non-null float64\n",
      "var_35     200000 non-null float64\n",
      "var_36     200000 non-null float64\n",
      "var_37     200000 non-null float64\n",
      "var_38     200000 non-null float64\n",
      "var_39     200000 non-null float64\n",
      "var_40     200000 non-null float64\n",
      "var_41     200000 non-null float64\n",
      "var_42     200000 non-null float64\n",
      "var_43     200000 non-null float64\n",
      "var_44     200000 non-null float64\n",
      "var_45     200000 non-null float64\n",
      "var_46     200000 non-null float64\n",
      "var_47     200000 non-null float64\n",
      "var_48     200000 non-null float64\n",
      "var_49     200000 non-null float64\n",
      "var_50     200000 non-null float64\n",
      "var_51     200000 non-null float64\n",
      "var_52     200000 non-null float64\n",
      "var_53     200000 non-null float64\n",
      "var_54     200000 non-null float64\n",
      "var_55     200000 non-null float64\n",
      "var_56     200000 non-null float64\n",
      "var_57     200000 non-null float64\n",
      "var_58     200000 non-null float64\n",
      "var_59     200000 non-null float64\n",
      "var_60     200000 non-null float64\n",
      "var_61     200000 non-null float64\n",
      "var_62     200000 non-null float64\n",
      "var_63     200000 non-null float64\n",
      "var_64     200000 non-null float64\n",
      "var_65     200000 non-null float64\n",
      "var_66     200000 non-null float64\n",
      "var_67     200000 non-null float64\n",
      "var_68     200000 non-null float64\n",
      "var_69     200000 non-null float64\n",
      "var_70     200000 non-null float64\n",
      "var_71     200000 non-null float64\n",
      "var_72     200000 non-null float64\n",
      "var_73     200000 non-null float64\n",
      "var_74     200000 non-null float64\n",
      "var_75     200000 non-null float64\n",
      "var_76     200000 non-null float64\n",
      "var_77     200000 non-null float64\n",
      "var_78     200000 non-null float64\n",
      "var_79     200000 non-null float64\n",
      "var_80     200000 non-null float64\n",
      "var_81     200000 non-null float64\n",
      "var_82     200000 non-null float64\n",
      "var_83     200000 non-null float64\n",
      "var_84     200000 non-null float64\n",
      "var_85     200000 non-null float64\n",
      "var_86     200000 non-null float64\n",
      "var_87     200000 non-null float64\n",
      "var_88     200000 non-null float64\n",
      "var_89     200000 non-null float64\n",
      "var_90     200000 non-null float64\n",
      "var_91     200000 non-null float64\n",
      "var_92     200000 non-null float64\n",
      "var_93     200000 non-null float64\n",
      "var_94     200000 non-null float64\n",
      "var_95     200000 non-null float64\n",
      "var_96     200000 non-null float64\n",
      "var_97     200000 non-null float64\n",
      "var_98     200000 non-null float64\n",
      "var_99     200000 non-null float64\n",
      "var_100    200000 non-null float64\n",
      "var_101    200000 non-null float64\n",
      "var_102    200000 non-null float64\n",
      "var_103    200000 non-null float64\n",
      "var_104    200000 non-null float64\n",
      "var_105    200000 non-null float64\n",
      "var_106    200000 non-null float64\n",
      "var_107    200000 non-null float64\n",
      "var_108    200000 non-null float64\n",
      "var_109    200000 non-null float64\n",
      "var_110    200000 non-null float64\n",
      "var_111    200000 non-null float64\n",
      "var_112    200000 non-null float64\n",
      "var_113    200000 non-null float64\n",
      "var_114    200000 non-null float64\n",
      "var_115    200000 non-null float64\n",
      "var_116    200000 non-null float64\n",
      "var_117    200000 non-null float64\n",
      "var_118    200000 non-null float64\n",
      "var_119    200000 non-null float64\n",
      "var_120    200000 non-null float64\n",
      "var_121    200000 non-null float64\n",
      "var_122    200000 non-null float64\n",
      "var_123    200000 non-null float64\n",
      "var_124    200000 non-null float64\n",
      "var_125    200000 non-null float64\n",
      "var_126    200000 non-null float64\n",
      "var_127    200000 non-null float64\n",
      "var_128    200000 non-null float64\n",
      "var_129    200000 non-null float64\n",
      "var_130    200000 non-null float64\n",
      "var_131    200000 non-null float64\n",
      "var_132    200000 non-null float64\n",
      "var_133    200000 non-null float64\n",
      "var_134    200000 non-null float64\n",
      "var_135    200000 non-null float64\n",
      "var_136    200000 non-null float64\n",
      "var_137    200000 non-null float64\n",
      "var_138    200000 non-null float64\n",
      "var_139    200000 non-null float64\n",
      "var_140    200000 non-null float64\n",
      "var_141    200000 non-null float64\n",
      "var_142    200000 non-null float64\n",
      "var_143    200000 non-null float64\n",
      "var_144    200000 non-null float64\n",
      "var_145    200000 non-null float64\n",
      "var_146    200000 non-null float64\n",
      "var_147    200000 non-null float64\n",
      "var_148    200000 non-null float64\n",
      "var_149    200000 non-null float64\n",
      "var_150    200000 non-null float64\n",
      "var_151    200000 non-null float64\n",
      "var_152    200000 non-null float64\n",
      "var_153    200000 non-null float64\n",
      "var_154    200000 non-null float64\n",
      "var_155    200000 non-null float64\n",
      "var_156    200000 non-null float64\n",
      "var_157    200000 non-null float64\n",
      "var_158    200000 non-null float64\n",
      "var_159    200000 non-null float64\n",
      "var_160    200000 non-null float64\n",
      "var_161    200000 non-null float64\n",
      "var_162    200000 non-null float64\n",
      "var_163    200000 non-null float64\n",
      "var_164    200000 non-null float64\n",
      "var_165    200000 non-null float64\n",
      "var_166    200000 non-null float64\n",
      "var_167    200000 non-null float64\n",
      "var_168    200000 non-null float64\n",
      "var_169    200000 non-null float64\n",
      "var_170    200000 non-null float64\n",
      "var_171    200000 non-null float64\n",
      "var_172    200000 non-null float64\n",
      "var_173    200000 non-null float64\n",
      "var_174    200000 non-null float64\n",
      "var_175    200000 non-null float64\n",
      "var_176    200000 non-null float64\n",
      "var_177    200000 non-null float64\n",
      "var_178    200000 non-null float64\n",
      "var_179    200000 non-null float64\n",
      "var_180    200000 non-null float64\n",
      "var_181    200000 non-null float64\n",
      "var_182    200000 non-null float64\n",
      "var_183    200000 non-null float64\n",
      "var_184    200000 non-null float64\n",
      "var_185    200000 non-null float64\n",
      "var_186    200000 non-null float64\n",
      "var_187    200000 non-null float64\n",
      "var_188    200000 non-null float64\n",
      "var_189    200000 non-null float64\n",
      "var_190    200000 non-null float64\n",
      "var_191    200000 non-null float64\n",
      "var_192    200000 non-null float64\n",
      "var_193    200000 non-null float64\n",
      "var_194    200000 non-null float64\n",
      "var_195    200000 non-null float64\n",
      "var_196    200000 non-null float64\n",
      "var_197    200000 non-null float64\n",
      "var_198    200000 non-null float64\n",
      "var_199    200000 non-null float64\n",
      "dtypes: float64(200), int64(1), object(1)\n",
      "memory usage: 308.2+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info(max_cols = 250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200000 entries, 0 to 199999\n",
      "Data columns (total 201 columns):\n",
      "ID_code    200000 non-null object\n",
      "var_0      200000 non-null float64\n",
      "var_1      200000 non-null float64\n",
      "var_2      200000 non-null float64\n",
      "var_3      200000 non-null float64\n",
      "var_4      200000 non-null float64\n",
      "var_5      200000 non-null float64\n",
      "var_6      200000 non-null float64\n",
      "var_7      200000 non-null float64\n",
      "var_8      200000 non-null float64\n",
      "var_9      200000 non-null float64\n",
      "var_10     200000 non-null float64\n",
      "var_11     200000 non-null float64\n",
      "var_12     200000 non-null float64\n",
      "var_13     200000 non-null float64\n",
      "var_14     200000 non-null float64\n",
      "var_15     200000 non-null float64\n",
      "var_16     200000 non-null float64\n",
      "var_17     200000 non-null float64\n",
      "var_18     200000 non-null float64\n",
      "var_19     200000 non-null float64\n",
      "var_20     200000 non-null float64\n",
      "var_21     200000 non-null float64\n",
      "var_22     200000 non-null float64\n",
      "var_23     200000 non-null float64\n",
      "var_24     200000 non-null float64\n",
      "var_25     200000 non-null float64\n",
      "var_26     200000 non-null float64\n",
      "var_27     200000 non-null float64\n",
      "var_28     200000 non-null float64\n",
      "var_29     200000 non-null float64\n",
      "var_30     200000 non-null float64\n",
      "var_31     200000 non-null float64\n",
      "var_32     200000 non-null float64\n",
      "var_33     200000 non-null float64\n",
      "var_34     200000 non-null float64\n",
      "var_35     200000 non-null float64\n",
      "var_36     200000 non-null float64\n",
      "var_37     200000 non-null float64\n",
      "var_38     200000 non-null float64\n",
      "var_39     200000 non-null float64\n",
      "var_40     200000 non-null float64\n",
      "var_41     200000 non-null float64\n",
      "var_42     200000 non-null float64\n",
      "var_43     200000 non-null float64\n",
      "var_44     200000 non-null float64\n",
      "var_45     200000 non-null float64\n",
      "var_46     200000 non-null float64\n",
      "var_47     200000 non-null float64\n",
      "var_48     200000 non-null float64\n",
      "var_49     200000 non-null float64\n",
      "var_50     200000 non-null float64\n",
      "var_51     200000 non-null float64\n",
      "var_52     200000 non-null float64\n",
      "var_53     200000 non-null float64\n",
      "var_54     200000 non-null float64\n",
      "var_55     200000 non-null float64\n",
      "var_56     200000 non-null float64\n",
      "var_57     200000 non-null float64\n",
      "var_58     200000 non-null float64\n",
      "var_59     200000 non-null float64\n",
      "var_60     200000 non-null float64\n",
      "var_61     200000 non-null float64\n",
      "var_62     200000 non-null float64\n",
      "var_63     200000 non-null float64\n",
      "var_64     200000 non-null float64\n",
      "var_65     200000 non-null float64\n",
      "var_66     200000 non-null float64\n",
      "var_67     200000 non-null float64\n",
      "var_68     200000 non-null float64\n",
      "var_69     200000 non-null float64\n",
      "var_70     200000 non-null float64\n",
      "var_71     200000 non-null float64\n",
      "var_72     200000 non-null float64\n",
      "var_73     200000 non-null float64\n",
      "var_74     200000 non-null float64\n",
      "var_75     200000 non-null float64\n",
      "var_76     200000 non-null float64\n",
      "var_77     200000 non-null float64\n",
      "var_78     200000 non-null float64\n",
      "var_79     200000 non-null float64\n",
      "var_80     200000 non-null float64\n",
      "var_81     200000 non-null float64\n",
      "var_82     200000 non-null float64\n",
      "var_83     200000 non-null float64\n",
      "var_84     200000 non-null float64\n",
      "var_85     200000 non-null float64\n",
      "var_86     200000 non-null float64\n",
      "var_87     200000 non-null float64\n",
      "var_88     200000 non-null float64\n",
      "var_89     200000 non-null float64\n",
      "var_90     200000 non-null float64\n",
      "var_91     200000 non-null float64\n",
      "var_92     200000 non-null float64\n",
      "var_93     200000 non-null float64\n",
      "var_94     200000 non-null float64\n",
      "var_95     200000 non-null float64\n",
      "var_96     200000 non-null float64\n",
      "var_97     200000 non-null float64\n",
      "var_98     200000 non-null float64\n",
      "var_99     200000 non-null float64\n",
      "var_100    200000 non-null float64\n",
      "var_101    200000 non-null float64\n",
      "var_102    200000 non-null float64\n",
      "var_103    200000 non-null float64\n",
      "var_104    200000 non-null float64\n",
      "var_105    200000 non-null float64\n",
      "var_106    200000 non-null float64\n",
      "var_107    200000 non-null float64\n",
      "var_108    200000 non-null float64\n",
      "var_109    200000 non-null float64\n",
      "var_110    200000 non-null float64\n",
      "var_111    200000 non-null float64\n",
      "var_112    200000 non-null float64\n",
      "var_113    200000 non-null float64\n",
      "var_114    200000 non-null float64\n",
      "var_115    200000 non-null float64\n",
      "var_116    200000 non-null float64\n",
      "var_117    200000 non-null float64\n",
      "var_118    200000 non-null float64\n",
      "var_119    200000 non-null float64\n",
      "var_120    200000 non-null float64\n",
      "var_121    200000 non-null float64\n",
      "var_122    200000 non-null float64\n",
      "var_123    200000 non-null float64\n",
      "var_124    200000 non-null float64\n",
      "var_125    200000 non-null float64\n",
      "var_126    200000 non-null float64\n",
      "var_127    200000 non-null float64\n",
      "var_128    200000 non-null float64\n",
      "var_129    200000 non-null float64\n",
      "var_130    200000 non-null float64\n",
      "var_131    200000 non-null float64\n",
      "var_132    200000 non-null float64\n",
      "var_133    200000 non-null float64\n",
      "var_134    200000 non-null float64\n",
      "var_135    200000 non-null float64\n",
      "var_136    200000 non-null float64\n",
      "var_137    200000 non-null float64\n",
      "var_138    200000 non-null float64\n",
      "var_139    200000 non-null float64\n",
      "var_140    200000 non-null float64\n",
      "var_141    200000 non-null float64\n",
      "var_142    200000 non-null float64\n",
      "var_143    200000 non-null float64\n",
      "var_144    200000 non-null float64\n",
      "var_145    200000 non-null float64\n",
      "var_146    200000 non-null float64\n",
      "var_147    200000 non-null float64\n",
      "var_148    200000 non-null float64\n",
      "var_149    200000 non-null float64\n",
      "var_150    200000 non-null float64\n",
      "var_151    200000 non-null float64\n",
      "var_152    200000 non-null float64\n",
      "var_153    200000 non-null float64\n",
      "var_154    200000 non-null float64\n",
      "var_155    200000 non-null float64\n",
      "var_156    200000 non-null float64\n",
      "var_157    200000 non-null float64\n",
      "var_158    200000 non-null float64\n",
      "var_159    200000 non-null float64\n",
      "var_160    200000 non-null float64\n",
      "var_161    200000 non-null float64\n",
      "var_162    200000 non-null float64\n",
      "var_163    200000 non-null float64\n",
      "var_164    200000 non-null float64\n",
      "var_165    200000 non-null float64\n",
      "var_166    200000 non-null float64\n",
      "var_167    200000 non-null float64\n",
      "var_168    200000 non-null float64\n",
      "var_169    200000 non-null float64\n",
      "var_170    200000 non-null float64\n",
      "var_171    200000 non-null float64\n",
      "var_172    200000 non-null float64\n",
      "var_173    200000 non-null float64\n",
      "var_174    200000 non-null float64\n",
      "var_175    200000 non-null float64\n",
      "var_176    200000 non-null float64\n",
      "var_177    200000 non-null float64\n",
      "var_178    200000 non-null float64\n",
      "var_179    200000 non-null float64\n",
      "var_180    200000 non-null float64\n",
      "var_181    200000 non-null float64\n",
      "var_182    200000 non-null float64\n",
      "var_183    200000 non-null float64\n",
      "var_184    200000 non-null float64\n",
      "var_185    200000 non-null float64\n",
      "var_186    200000 non-null float64\n",
      "var_187    200000 non-null float64\n",
      "var_188    200000 non-null float64\n",
      "var_189    200000 non-null float64\n",
      "var_190    200000 non-null float64\n",
      "var_191    200000 non-null float64\n",
      "var_192    200000 non-null float64\n",
      "var_193    200000 non-null float64\n",
      "var_194    200000 non-null float64\n",
      "var_195    200000 non-null float64\n",
      "var_196    200000 non-null float64\n",
      "var_197    200000 non-null float64\n",
      "var_198    200000 non-null float64\n",
      "var_199    200000 non-null float64\n",
      "dtypes: float64(200), object(1)\n",
      "memory usage: 306.7+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info(max_cols = 250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20098"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train.loc[train.target == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10049"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train.loc[train.target == 1])/len(train) #highly imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c88af28c50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (30,10))\n",
    "train.drop(['target', 'ID_code'], axis=1).plot.box(ax=ax, rot=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c88a8e71d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSVqtRju5QghPAN4EbAe8I8a4ear9DsC7gYcD3wWOjDFendv+BHge8DPgd2OMnyiWvaR15eHzJEmSJEmSJEl9tamuQwhhO+AE4InAA4HnhhAeONXtecBNMcb7AW8Ejs+3fSBwFPAg4AnAW/J4kiRJkiRJkiRJ0prV7uQCHgVcEWP8eozxp8BJwNOm+jwNODGf/xBweAgh5OtPijH+R4zxKuCKPJ4kSZIkSZIkSZK0Zk0OV3gf4NrK5euAX5jXJ8Z4awjh+8Cu+fpzp257nzVnK0mSJEmSJEmSNpw9zr5wxeXrD33okjJRn4QY4+IOITwbeHyM8fn58v8LPCrG+PJKn625z3X58pWkb2y9FvhijPG9+fp3AqfGGD88FeOFwAvzxZ8HvjaVxm7Adxak2bbdGGXHGEuMEmOMJUaJMYzR7RhjiVFiDGN0O8ZYYpQYYywxSoxhjG7HGEuMEmOMJUaJMYzR7RhjiVFijLHEKDGGMbodYywxSoxhjG7HGEuMEmOMJUaJMYzR7RhjiVFijKHG2CfGuHvNmEmMceEJeAzwicrlPwH+ZKrPJ4DH5PPb52TCdN9qv9WcgC+vZ7sxhpentbAWY44xlDytxfhiDCVPa2EtxhxjKHlaC2sx5hhDydNaWIsxxxhKntZifDGGkqe1sBZjjjGUPK1F97WYd2rym1xfAu4fQrhvCGFH4CjglKk+pwDH5PNHAGfFlNkpwFEhhDuEEO4L3B84r0FMSZIkSZIkSZIkaa7a3+SK6Te2fof0LaztgHfFGLeGEF5L2rt2CvBO4D0hhCuA75F2hJH7fQD4KnAr8LIY48/W6b5IkiRJkiRJkiRpg6jdyQUQYzwVOHXquldXzv8EePac274OeF2LHAHevs7txig7xlhilBhjLDFKjGGMbscYS4wSYxij2zHGEqPEGGOJUWIMY3Q7xlhilBhjLDFKjGGMbscYS4wSY4wlRokxjNHtGGOJUWIMY3Q7xlhilBhjLDFKjGGMbscYS4wSY4wlxlwhH+9QkiRJkiRJkiRJGowmv8klSZIkSZIkSZIk9Yo7uSRJkiRJkiRJkjQ47uSSJEmSJEmSJEnS4LiTa4MIIdwjhHD3ZecBEEJ4WMvb7xxCeHjb+xNC2G3O9XcPIdy15rbW8/bjrLmefRJCuGeBMXYtkcsYtK3nsmvpXJ85zprmep9qCf2o57xa5rYNU89lb5t9M4bnoT5tn32Y63mcVW2fIfmFEMIzQwjPyOdDw1j7V87vMC+XEMKmEMKmfH7HEMLDQgj3qBn7pQvadspj3K0yZqi0HxpCeGUI4YmV6w5qcH/2roy5bwjhiBDCg2f0e0Su1VOqNchtu4QQjgwh/EEI4RX5/N3qYq9GCGGPEMIe+fzu+bF70IL+/71k/KZCCP81hPDz+fwvhRD+MITwpFWOYT23xW1VzzHM9cq4c+d7h3N9TfWcMc6GWDsX1TK3O9e3xW0719e9ljlO7+vZtpb5dr3bNnO/odazd2vnonUzt69q7WyybuZ+rp0Fldg+G4sx9uoEPB54K3AK8NF8/gkNb/vqyhjPA/adav9tIADPAZ6dzx8OvBl4KbBpwdhnVc7vNtV2dB7jhXnMZwD3yG27A+8GtgAnA3vm698APHZBvHsArwaen8f8M+BjwN8Ad6/0OxT4h1yrDwObgfvltr2Bk4AbgcuBK4Ab8nX7zoudb7sl/90r9z8H+FNgh0qfjwD7A6cBHwf2A/43cDNwHnAA8LCp08OB64CDgYdNHpfKmHsCZ+YxvgA8AHjvpOb5sb0WOAO4Bnh2vv57wDvy4xlm3J8nAlcBn8uxtwJX5lwOB+6dH6fvAz8DvpFPx03uc9t61tUy/116PetqWaKewC6kbfUy4Lv5dGm+7m4N5vppwM7AXwPvAf6fqfa3AHuQ1o8TgF1z7C3AB4Cfq8yz6mlX4Grg7vnyEypj7gK8E7gYeD9wr3z95ko9HwF8PW8b1wCPA84HXgXsN+e+PAI4Oz8uewGfynX7EnBw7rMT8Npc5++TtsFzgd+s5LamegKn5b9Lr2ddLfP1repZV8uNtHbSzVxfcy27XDvrapmvb1XPulrmPkuvZ10tc79W9ayrZYfP60tfO6lZN/M4rdZOatbNvqydjGSuF1o7fy3X77Qc6x3A6fm6X2sw179Beo1wXX48Pll9DPI28XTg28C3gKcB/wyclW/zlNzvD6ZOrwS+U7n8lsqYv5Tjnp1r9uvAReTXLcAf5Tq/irR9/XW+/mf5fv0l8MAZ9+XYXMvLSK+LLiPNga3AH+Q+jwO+nB+nm0ivmT4PfJq0Xf1Grv9bc/xXAW/L1/1GTS1/tXJ+Z2bMI+Ag4EU5z6uBl+R6vgv4Gul16ZunTn9P2vbeDLx5Tuz7As8E9q/MsTvm8wH4rTzOS4Dt8/VPnfSZM+bf5cfhvFzzLwB/nmv3N7nPTsARwCuAlwNPoPI6uUQ9F9Uy/116PetqWaKejGSu5+sXzne6metrrifwjfx36fWsq2WJtbOulqXm+hDWTrqZ62uu5TLXzulalqhnk1r2oZ51tcz9WtWzrpb5+i6e15e+dlKzbuZxWq2d1KybfVo7GcFcL7V9rua06hus5ynf+VOBo/IG+0v5/KnAmxrc/hvAfwc+m8e6Enj51MR6C/Ah0k609wIfzBvgSZMYpDcOqqctwH9ULp9fGfNVwCeAY/JYbwS+Wmk/OT9QewK/CXwqX39jnhjXAP+D/GZ25XanAsfnSfHpvCEdQnqD4aO5z2bgH0k72T5E2gH2AuAC0ovwLwJHAttVxt0u1/TcvAHPOj0LuDH3/xTwYuChOYcvALvmtgtyrZ8CPDffl6Pyxv8U0hsHt+XbnF05/Tj/PWvyuFTy+wBpsm4i7Sw8k/ymcW7/AnmxBHYDLsrnvwb8Dmlx+SbwJuDRldtdSHrz4zGkN7Eena8/IG8XZwG/nK97Zn4c7wL8FfD2fH2retbVMv9dej3ralminqQ589+APSpj7pGvm8yR6Teqqm9YfYttO3WfTprPHwbuUJnrp5MWyGNJ8/a/kRbpl7NtDt1GemKonm7Jf78+Vct35Pz3Ic3pyRtu1XqeDTwyn38AaY5fBfwtaX06L9/23pXbnEd6M+y5pCfnI/L1hwNfzOc/Slo/9iQ9sf45cH/gRNKat7CedbXM/Zdez7pa5vOt6llXyxJzfShrJ93M9YW1rNxuqWtnXS1nzPVV17Oulvn80utZV8vKXF9zPetq2eHz+tLXTmrWzRlzfdVrJzXrZl/WTkYy1wutnZcyY8cg6QXopfn89IvX6ovYH5B2UD4o9z2CtONxkscF+bRHHvMHwM/ntn3Y9pz7Q9JrmVcDr8mnmyrnz5/a9iYfwvgvpG3vkkr7l4E75fPbAxdXcnkw8DrSGw8XkebLpO5bgTuRdgD/ENg9X3+Xyfh5jN0rNfqnfP5XSW+0fI0ZO65JO5P/Zfr6qT6TN2+eA/xrfmy3kudZZa5vAe6c8/w38rqSY1xIehPnvaTXncfk042T87nvRypjPo00d/8x5/+bwCXAnXP78aTXf0eT3tR4V77+x6Q3hd5DesNnu6n7s5U0Z+6cH8vJeDvk8Z9D2nbeQXot/R7gfaT168DK+rzmetbVcjIPl13PulqWqCcjmev5/ML5TjdzfWE962qZ+y69nnW1LLF21tWyxFwfytpJN3N9zbXscu2sq+VkrrepZ10tK9vNUutZV8t8vlU962qZr+/ieX3payc162Z1O1/r2knNutmXtZORzPVS2+dqTqvqvN6neQ92Lsjl+fwP5px+CNyaH+jJHu+7kXYWvbEysSafst+B9KJzx8pGP2mb7ADbnzTh9iW94N8nny6YWvzuUhlzC/C1SvtXpu7LZDGcvFi/P+mNgq2kPcSvIb0pcWHlvn9zzhjVF+HbA5+vbNSXTGo2p6aXk95E+d95Q54+/bAaq3K7o3Ou++X7Xq3FFVN9zyctbp8hf8IrX3/VdL/p+1a5fEGOt3O+/DlWfuJg64wx9gb+OMf/OmknQLX92ul6kt9Um/W4AZdNatamnnW1rG4Xy6xnXS1nxFh1PanMkRm1/Fr++zPSm0Bnzzj9eMZ9+zPSG067ztg2vzFnDv0h6Q3IAyttV1XOL6rlhZX7M1lzzp3qs2VqjENIO9qvz/fjhTV5TtaJ6Xp+Kf/d1KSedbWcc/86r2ddLWeMsep61tWyxFyfc/96t3bSzVxfWMv8d+lrZ10tKzVbcz3rajlnrndez7pa5r+t6llXyxljrNfz+tLXTmrWzQb1rF07qVk3Z8yhpaydddvmUOb6jDhrWjsnj+lUvx0n9zvX7IVse7FaPX1nRowHkbbrZ8zYNi+Z6nt+5f59iPTCdvKi9Otz6jn9mucC0k7CB+fLp7Pt07V3ZNsbWedP3e5RpCNeXJtvP3lDdzvSNwM3Tec96VPpV81rK/AvwC4z6rlLrvUpc07/B/hR5XH7uUqOlwHPrNzXaszp2l8A3JX0Icz3A/eZruWM7fsLwH3z+ckO1uoHKb8yVYuLKrHuTvrg45mkT02/jW3fiJ/U7I6kNxvuVKnbV0lvKty5EvcT+fxBwBfy+Vb1rKvljG1rKfWsq2WJejKSuV6p8dz5TjdzfWE962o5Z3sb5dpZV8sScz337f3aSTdzfWEt8/mlr511tcznW9WzrpaTub7setbVMv9tVc+6WlZyWe/n9aWvndSsmyXWTmrWzcm2t+y1k5HM9VLb52pO29MvPwkhPCrGeN7U9Y8EfpLP30zai/nt6RuHEK4lTcxbAWKMN4cQngK8PYTwQdIE/WluuyWE8KUY4+TyrSGEn+XzTw0hPIP0Scq/jTGeEkK4JcZ4TY5zpxDCwaQX9tvFGH9UGfNnwKdDCK8lfR3y0yGEp8cYPxJCOJR0WBKAmG9zOekre3+Zjw/6XNKOuX8P6dj/dwV2CiHsG2O8OqTfWNgxj3FbCOEeMcbvkQ57sl0e86Z8HNKvhBDeQvqU7bX5NnuRFqILSDsH/zbGeMmMWv5KPrtDCOGOMcaf5LHfG0K4nvTp57uQ9gRPvGFqmB1jjB8KIZye799vkb5yGqf67RlCeDNph97uIYQdYoy3TOIDfwGcHUI4gfTG0QdDCB8FDiMtXOTbknP8Bunbcf8jH/fzKODmEMKLSF/5vCmE8ArSJ3h/hbTX+ychhKNJb2Y9i/SVT3IdJ79d17aedbWE/BguuZ6H1dSSAvW8OoTwx8CJk7kcQrgX6dMsk9peCrwoz5Hpel4L3CGEsCnGeFvO9XUhhOtIn/LeibRWTLx7aohN+TZ/G0I4CXhjHvM1U/W8ZwjhD3Itdw4hhJhXXLZtFycAp4YQNgOnhxD+Dvj/SZ9+v5D05D6p5znAOSGEl5M+6XFkrtWvkZ7wYmW9eBzpDVaAH4UQfinG+Lm8pn0vj3dbruk1NfW8paaW9KSedbVcYY31/HFNLWHjrJ1dzPW6WkL6x2bZa2ddLSlQz7paAtzYg3ou3DbzbdrWsy/P60tfOxusm9B+7ax7Hno7/Vg7xzLXof32+S7gS3nbqNbyKNIhUyB96vGSGOMXZtTzOOCWEMIeMcbrc65bQwiHkw6rsh9wRWXb/e3Kbbdj21z/BnBECOFpwKdCCG+cCrV/COHiXM99Qwh3z69DNuV6HgO8L4RwEemNgi+HED5DeuE6+V2AFb/3kF8HnhdCeCXwX4HfCiG8n7QNnAmcmB/jw0gvjsnjvjO3P410FAxCCHcmbTOvA84PIXyyUs+9SXPgL0nfpDuabWvxf5aD9OYCpNd835rkGNLruo+FEPYkbWO3Vban//ydgRDCHUlz/YfA74cQHg68N4TwcW7/+9jVbXX7GONVOd53Qgi3AdeGEA6LMZ5F2mb2Iq1j1d/gizHGm4D/BfyvkH6r4TnA5pzr+0MI55DebHgH8IEQwrmkw+98Fngs6cNkkN5ouWce9OIQws75+rb1rKslPannwlrGGPcCPt6ynmOZ65C+ibtovv/Rigdnfeb6rHruTXqueydpG11US3pSzy7Wzn+rqSVsnLWzi7leV0tIH0Ra9tpZV0va1pP656HJfV52PRfWMt+ubT378ry+9LWzwboJ7dfOuuchSNvVstfOUcz1Qmvn6sRV7hVbzxPpEDD/TNpwPsm2r5z/M/Dw3OevgEfNuf3xpAn0uBltf0U6hMlpwE4z2vcAzpu67i6kF9CnANdVrj976jTZw7or6SuRO5COqT85vv5tpD3f7wf2jlN7Tefcl+eS9oJ+m/Ti9wzSYVy+ybZP3R5JOlTLJ3OcJ+Xrd8+xdiQdK/N00id5L8n3/6XAHUiL/t5z4j8i/33FnHoenPN50Zx63g/4u6nrHprrdcPU9dOfBJjsbd+DbZ+IvV9+fP+JtHf7rcDjK2O8oaaeewH/M99uj3y/LiH9psMBpAXnA/m69049ps/K51vVs66W+fzS61lXyxL1JO3tP570iYTv5dOl+brJb5AcQf4K84z4Tye9qfQrM9qeQPp0xGsX1PJDM65/CulwStdXrnvN1Gn3Si3fXel3KOmr1RfkbeO0/FjuAJxUU8uHkN7AO4307dE3kd4U3Qr8Yu5zEOlQUt8nfap88tXu3YHfratnXS3z317UE/jlGbV8Idt+Q6dVPSu1vDnX8gHVWi6Y66ez/LXzxgHO9YW1zH2XtXbeGFdRy7b1rKtl7rOWev7n81CJeq6mllP1vKFy3VCe16fXzptY4trJjHUzX99k7Vzz81C+/aK187HUPA/l863Wzrptcw1z/WCWMNents+3tdg+H0g6hMrfk35/91gqvx+Qt9E7L8jhV4CHzLj+bqRvHD6SGcfRJx3B4ugZ19+ZdGj0z1au22fqNDlCxm5s+8TpdqRDYf4eaafjkVQO4cLUb9nNiLs96XXRUfn8L+Z6/DErj6bx0nz9C8iHSyEdbmafynw/Kufwh/n85PE/DTh0TvzP5r9fYOq3EUgfRjyTdFj7vZn9Kej7MLVGkN7EeBnw3qnrf8a2I5T8lG2HntmR9AGGvUjb9GfzdnkTaUfpBWz7bce5rzErtXgM2w4htF+ux3NIb34cT1oL/pT8++VVfQIAAB2LSURBVHiV7W1rZaw117Oulvny3lR+l28Z9WxSyxL1ZCRzvW6+091cP2BePetq2ad6LqpliXo2qWXbuZ7/9n7tpLu5PreWTeo5p5Y7c/u1c831rKtlPt+6notqma9bVj2nt81Gz0NrrWddLXPfrp7X5z4X0fHayYx1c3J/abF2UrNu5j7Ta+dj6XjtrNs2K3N9zf8n1W2bpeZ6qe2z6SnkG/dK3vt3H9IDcV3Me4Ir7Q+KMW6dc9s75bP/ZbpPCOE+McZvzhojhHAX0j9PN0y3hxAeAjwmxvi2RXnkPdB3iDH++6SddBzM7WOM35267U6kB33m/aiM96CY9mBuT3oj6Zsx79HNfR5D+qTxFTHGm+eMM7deXQshBOCuMcYfLDuXMbCeZeX1Y78441PmWj3rWY5zvaxcz51i+oSTWnL7LMd1s6wxbJshhHuw7ZOSa+rTtn0oMZr2WYv8evBHMcYrpq7fAXhOjPF9JeNNxbgb6cMQX8yXDyAd3n570mvNL8X8bdIQwi/HGD/dMt6vk97suijG+Kl83SbSmyn/0WbsPNZDgH+PU9+S7aKWOU6jepaoZR5/XeupxUIID4sxnr/W9r6M0ZcYqzWEtXMZcz1/Q+H+pMOHNXq+Wkst6+I0zWO6lvm6IvWcl8OYn4uG8LweQtgtxvidBWMubC8xxhhihHSEtltX877DarfNuhiryWGd5/rMPIrO9dXsEevLianjaK6lT9v2ocSY1wd4dc1tFraXGKOLGKXzBB4PPI/KXul8/W9Pte+7lvYljLGm+9E2BmkH9nOAZ+fzh5N+uPKlbPv0zsI+bdtLxFjnPF8yI8YR8/KYsd2eVbNdL2zvyxjLiAHsNnX56FzvF+b6L2wvMcYAYrygYYxnsO3bmbuTDmO2hfSNkz3z9dN93l3tU9e+xjFOLBBjVWMUuh9vAB5bsz0v7NO2fWQx7kH6cePn5e31T0lHBPgb0qfvJu3Pz+1/Vm2fGmNmnzW0/2mLGCXuR90Yz5s3Rh7nUNInGz8KfBjYDNyvVPtQYizos1+TMUifzjyJdKiVy/PphnzdvlN9bsztV1T7tG0vHOOGAjGajDG3z4J1YMui9hKnuhhd5FAiz0q/vXJtzyGtFTtU2j5SKJeFMbrIoYtakL45exrpW577kX638GbSN2cPaNKnbfuCGDcVGKNtnjetMsbDZpyuI32zd1bbw6vtOcbCPmscY1V51N1+jXmupRbV1/73IX2L4CbStwweUGgOVWPsmWPcPIkxp71oDi3ybJwH6Vvbu+XzjycdwuwM0pGZnj3nNjvnx+XuTdvr4qw2j7oc1pJniVqQjsDwDtL7ImGdHvPaGF3kUSLPBmM8EbiKdLSGg0lHcrgyrwmH17WXGGNO+xWFYzxhjffjiqYx8hj3Jr2O/z7pG1OTo7wdx9S3r0ivs+664LGZ2V4Xo2QO65nnavOo3ZaXMQkLTOKFh/pr0qdt+1BizOvD1I+Kr7a9xBhdxCiZJ+kYrp8l/XjflcDLK33Ob9D+14va89+FfepilBijYZ5ta/EW0o9KnkL6J+eDwG+QXiS+Kfdb2Kdte4kYPcrz4qnTFtJX7Kevn9meY6zXGD9pOkYXeTaMUf0Rz1eRvj59TK77G+vaS4wxohjVHys9mXTIrj1Jv3k0OfzYwj5djDGgGDeSDot8DekweQfPeM5a2Kdt+8hinEo6RMJbScdT/3vSIfVeS9rxsLC9xBhjiZHH2Az8I2ln94dIO8BeQDqkxbPbtg8lRqE8v0g6vMp2le11O9KhTs7Nlxf2ads+lBgNx3jmnNOzmDoU8Ix1onbHD+l/iYUx2uTQJI9SeTaJQXo+ejHpKCN/T3qTd9fcdgEFdlA1iLGwPf9tlUehPOtifJZ0qNrnkp6vjiJ9kOApwJm538I+bdtLxOhRnrflx+DsyunH+e9Zde05xtLH6FGM6v/5HyAdMngT6UNZZ1KzgypfX7cTqy7GwvaGMRburGt4P1rVgsoana/bN5/fjfTtBSiwg6ouToP2JjFa5VmoFl8Dfof026nfJL0n8uip56yFO38atDeJsbBP2xxK5NkwxoWkDwo8Bvgu2w4tdwDp/buF7SXGGEuMfP4s4Jfz+WeS3he5C+lnlN5OgR1UDWK0yiHfros8G+8Ea3JaVee+nGAc37Ja7xik42v+gG3H2pycfgjcOnXd7dqnxpjZp217iRgd5rmFfHxj0nFlT2XbG8EXtG0vEWMoebLtRfIOpCeGybF0t6+0LezTtr1EjB7leQrpH8H9Sccm3pf0j+A++bSwPY+19DF6FOM/PxhA+kem+jsAW+raS4wxohhfq7R/Zeo56sL8d2GfLsYYUIzJGnx/4M9Jnxy7jPQ7TZMX4Qv7tG0fWYxJ3QPpcNDT9V7YXmKMscTIf6tvWmwPfD6fvzvpt6datQ8lRqE8L6/WeKrel1f/zuvTtn0oMRqOcQvpGx//OOP0Qxrs+Knr0yDGwvaGMbrIs0mMC6dqfDRpjd2P9L9AiR1UdTEWtufrWuVRKM+6GNX/o66YGuv8aqx5fdq2l4jRozyPAD4D/Hrl+qsq5xe2N+nTxRg9ilHdsTO9rV8w1T5vB9RqdmLVxbhd+xpirHVHWqtakNaFnXP756gcjYVtvzHVagfVZKxFcRq0N4nRdkdaiVpU67036feSzge+zrbf/63b+VPX3iTGwj5tcyiRZ8MY1TGunWq7sK69xBhjiZH/XjR1/Vcq5y+j5Q6qhjFa5ZCv7yLP2jFWc1pV576cqhvVWvu0bR9CDNLez3vN6kN6U/cbwL3m3Pba6hjz+rRtLxGjwzwvnbp+O+CdpG8sbG3bXiLGUPJk5QuU06f6Tp4YFvZp214iRl/yzH+fQfp041Pz5a9P9VvY3pcx+hCD9GR7MOmQCNNPyhfWtZcYY0Qx/ifpmx53Al4PPD23HQp8Jp9f2KeLMQYUY9bz+UGkb+BekS8v7NO2fWQxLibtVNib9OmxffP1uwJfrWsvMcZYYuTzF7HtcJt7k79lky9vbds+lBiF8jyJ9C3uXyB9wvHe+fxbgA/kfgv7tG0fSoyGY3wFePD0mpBvey3NdkDV7Ryqi7GwvWGMLvJsEmMrUz8eT/rB+SuAb1FmB1VdjIXt+XKrPArlWRfj4krbS6f6TnaaL+zTtr1EjL7kmf/uRHqT7IOk9XX6//yF7X0Zow8xSId9fTNpB+03WflNxEtY/Q6oWTuH6mIsbG8Yo8SOtFa1IP30wFdIP91wPOkwxb9BWm9fn/u12kGV/y6M06C9SYy2O9JK1GLmEa2AnwdeM2O7mLXzp669SYyFfdrm0PB+tMohX38WacfsH5H+538F6VuPx+T6L2wvMcZYYuQxziA9398beDnw4Xx9AP6FljuoGsZolUP+20WetWOs5rSqzl2c8h3dq6bPuYv65DEW7fxZ2D6UGHVjkPZ8PorKC+dK2/GT9jm3Pb46xrw+bdtLxOgwz48Bj5tT59vatpeIMZQ8ScdS32lG+x7Aefn8wj5t20vE6Euelct3If0OzSnAdTP6L2zvyxjLjsHKw3acDfxcvn5X0uHPFraXGGNEMXYgfdV88rXz20hvsr0f2Dv3XdinizEGFGMUh1LuUYznAt/Op2eR/gn/FOnNixfWtZcYYywx8hhHkg4j88m8/T4pX7973oZbtQ8lRqE8dyT9NufppG/FXkL6X+ClwB1y34V92rYPJUbDMQ4hr7Mz1oFH0GwHVN3OoboYC9sbxugizyYxXsHs//MPJq0LJXZQ1cVY2J7Pt8qjUJ51MV7E7P/x7wf8XT6/sE/b9hIx+pLn1HUPJf1vesOc7Xlhe1/GWGYM0pu21dPk95D2IL253mQHVN3OoboYC9sbxiixI61ELe5Heu/pn4D/QzoE9OMr/VrtoJqaD4vizG1vEqNEngVq8YZ586EyRt3On7r2JjEW9mmbQ8MxWuWQz+9F+rDlW0nb9CtIc+PjpMPwLWwvMcZYYuQx9iZ9yOoS0lGEqu+RTF5HrXkHVcMYrXLI57vIs3aM1ZxW1bmrE1OH6FlLn7btQ4nRtE/N7R/Upr3EGF3EaDsG6ZP1D5vTdp/cfqdZYzRpLxFjSHnOqjdpx8M9Fz0edX3atvdljDYxgIcAL563fde192WMvsSoXLcdcOcFj9nC9hJjDDkGsAv5ED0LHo+FfboYo88xyG/szHpMqn3q2heNsZFi5PYD2XZ43e1Jb/L+3NS2fNC89hJjjCVGvv4x+fq7zan3PUhvXKypfSgxCo5R+7+tp/Ynmu2Aqu2z3nl0kWeJ+0mBHVSF6tkqjxJ5dnVfPc2tfyB/G2Qt7X0Zoy8xZtzmGOp3QNX2KfA4L4zRtr1ULRqOs+YdVKVOTWJ0kWfbMajf+VO7E6tALVvn0DbPLu6np1U/Jq12UHWRQ1d5lr6vS39w59zJE4BHtunTtn0oMZr2qbn96A/dOKQ8rcX4YgwlT2thLcYcYyh5WovxxRhKntZiGLUAXt1g3IV92rYPJUapMdqe+pBDiTy7GsN6Nr/9RpmHY4kxlDxLxChxcq5vvHqO5XloI9VzKGtSH2J0tV0MYa6vVx6b6KdDgS+GEK4MIVwcQtgSQrh4lX3atg8lRtM+i4SW7SXG6CJGiTHGEqPEGMbodoyxxCgxxlhilBjDGN2OMZYYJcYwRrdjjCVGiTHGEmOtYzy/wbh1fdq2DyVG6zFCCK+uC9CgT+v7URejizybxGg7RokYDdpb59FFLepu37DP0udQoTHGEqPEGIOIMZa1sy9zvQ9rZ4nHtItalBiji/V5LM/rDdpLjDGWGLV9NtBcL5LHtO1Xe4OOPLFAn7btQ4nRtM8isWV7iTG6iFFijLHEKDGGMbodYywxSowxlhglxjBGt2OMJUaJMYzR7RhjiVFijLHEmNsnhPCDfPYulfOQdnrdaarP7W4O3Klt+1BilBpjgecDr63rE0L4w3XMoUkeRfJsksM617NRjB7Us0gtmjweG2UejiXGUPIsEWNO28RY1s7O5nrdGCx/7Wz0mNb0KVKLLupZYn1um0NNO3T0vD6UNakPMZqMMadtYjRznfX/n/F2ermTK8Z4DUAI4Z7AHdfSp237UGI07SNJkiRpEG4GHgmcFmN8WLUhhHBttU+M8dvTN859Ysv2ocRoPUahNyz+te396OiNlYV5NnwzYmG9C73x0nq76OLNsLo8C2w3tTEYyTwcUYyh5Nk6xljWzr7M9T6snR3tBGhdixJjdLE+j+V5nYGsST2JUTvGBprrtXm23Al2O73cyRVCeCrweuDewA3APsClpB9rb9SnbftQYtT1CSEEYM8Y42SyTde6yaFUfhpC2GutY3QRYyh5WovxxRhKntai2xhDyXMsMYaSp7UYX4yh5Gktuo1RYIx3k/6f/+mMtvdP9bndi8bc55aW7UOJUWKM59L+TY8S96OLN5nq8mxSi7ZjNLkffahniTzralF3+yYxxjIPxxJjKHmWiDGWtbMvc73Ec0Afng/r8ixRiy7qWWJ9LvF82Ifn9aGsSX2I0WSMPvyf1MVcb5Jnkzyaiz34sbHpE3ARsCtwQb58KPD21fRp2z6UGA3H+EpNvRe2lxijixhDydNajC/GUPK0FtZizDGGkqe1GF+MoeRpLYZXiwbjP6htn7btQ4mxqA/wV8CjZrUDx1f7zLn98SUesxzjyHkxmuTQNs8mtWg7Rqlarnc9S+RZop5197Npn77Pw40WYyh5Nphjg187+zLXSzwHtK1nice0YD3n1qJP9WwzR5rk0DbPEttmk/vapL3EGGOIUWK7aPuY9WyuF9k+Y4y93cn15fz3ImBTPn/eavq0bR9KjIZjnEDaMzqv3gvbS4zRRYyh5GktxhdjKHlaC2sx5hhDydNajC/GUPK0FsOrRd0JOL9tn7btQ4lRKM8Sb3qUuB9dvDlTohatxigUY93r2VEtnIcjizGUPAvFGMXa2aO5vvS1s9Bj2kUtutgu1n2ODOh5fShr0tJjdLhd9H6ul8ojxkjInXslhHAG8HRgM+kbSjeQXgD+YtM+bduHEqPhGF8FHgBcA/yIdGzLGGM8qEl7iTG6iDGUPK3F+GIMJU9rYS3GHGMoeVqL8cUYSp7WYni1qBNCuCDGeHCbPm3bhxKjUJ7nx6nfSFttn0L3oy5GF3k2idFqjEIx1r2eHdXCeTiyGEPJs1CMUaydPZrrS187Cz2mXdSii+1i3efIgJ7Xh7ImLT1GwzE2xFwvlQf09De5gM8CdwN+Dzga2AV47Sr7tG0fSowmfZ7IYnXtJcboIkaJMcYSo8QYxuh2jLHEKDHGWGKUGMMY3Y4xlhglxjBGt2OMJUaJMcYSo9QYizT5tGJdn7btQ4lRYozQIEZdnxL3oy5GF3k2idF2jBIxuqhnF7VwHo4vRokxhhJjLGtnX+Z6H9bOEo9pF7Xoop5dzJGhPK8PZU3qQ4wmfTbKXC+VR293cgXgE8D3gJOAk2OM311ln7btQ4lR2yfGeA1ACOGewB2nblvbXmKMLmIMJU9rMb4YQ8nTWnQbYyh5jiXGUPK0FuOLMZQ8rUW3MUqNoV4p8YZFCV28sbLet28yRleHnOnizbC2OUhDNpa1sy9zvQ9rZ1c7AUrk0XaMPtRzKM/rKmsI22aTPt3N9djgmIbLOgEHAa8DLgPOWEuftu1DibGoD/BU4HLSYVCuAm4DtjZtLzFGFzGGkqe1GF+MoeRpLazFmGMMJU9rMb4YQ8nTWgyrFqQPse1VHW/6BJy7qE8eY+4x7uvahxKj5Bg17XW3D8CW9cyhYR6t8mxaiybbZ9v7sd6PaaF6tqpF08djI8zDscQYSp4lYkz61LSPZe3sZK6XuB/rvXbW3b6uT4ladFHPEutziXq2nUNNc3DdKxOjy+2i73O9SZ6l8ogx9n4n1x7Ay4HPAxevpU/b9qHEWNQHuIj0W10X5MuHAm9v2l5ijC5iDCVPazG+GEPJ01pYizHHGEqe1mJ8MYaSp7UYZC2+Uh1v1qmuT9v2ocRoOwYFdirmPv/WMseFeVDujZW5ea6iFmuu5yruR9ttr1UeJfJsWM+F203D+zr4eTimGEPJs22MsaydfZnrfVg7SzymDfNsVYsO69lqfS74mC71eb3ufjbts1FidLhdDGGul1g7a3dYxxjZRA+FEF4SQvg0cCawG/CCOPUDzHV92rYPJUbDPrfEdPjCTSGETTHGs4GHrqK9xBhdxBhKntZifDGGkqe1sBZjjjGUPK3F+GIMJU9rMbxanBtCeCSL1fVp2z6UGK3GiDFG4COLBo8xPrquD3Bim/tRl0duj+uZ5ypqseZ6ruJ+tNou2uZRIs8m9aR+u2FRjELtfRljLDFKjNH7GGNZO/sy1/uwdpZ4TJvkSctalBijRJ5ZF8+HS31ez3q/JvUoxsI+G2mul1g78xi1+vqbXPsAvx9jvLBFn7btQ4nRpM/NIYSdgHOA94UQbgBuXUV7iTG6iDGUPK3F+GIMJU9rYS3GHGMoeVqL8cUYSp7WYni1OBR4UQjhGtIhDQPpteBBq+jTtn0oMUqMcW4I4ZExxi8xX12fEvejLkYXeTaJ0XaMEjG6qGcXtXAeji/GUPIsEWMsa2df5nof1s4Sj2kXteiinl3MkaE8rw9lTepDjCZ9NspcL5VHrRBj3U5rDV0I4dXAu4BvAUcDuwDvi+mTo7XtJcboIsZQ8rQW44sxlDythbUYc4yh5GktxhdjKHlai0HWYh9miDFeU4mxsE/b9qHEKJTnV4EHAHPfsKjrU+h+1MXoIs8mMVqNUSjGutezo1o4D0cWYyh5FooxirWzR3N96Wtnoce0i1p0sV2s+xwZ0PP6UNakpcdoOMaGmOul8miir9/kUlkB+ATwPeAk4ORYeQHeoL3EGF3EGEqe1mJ8MYaSp7WwFmOOMZQ8rcX4YgwlT2sxsFpUXvjdE7gjM9T1ads+lBiFxnjirHFX06fE/WiQx7rn2SRGgTFax+ionuteC+fh+GIMJc8SMRjP2tmLud4gjy7WztaPaV17iVp0Uc+O5sggnteHsib1IUbDPhtirhfMo15s8MNdnsZxAg4CXgdcBpyx2vYSY3QRYyh5WovxxRhKntbCWow5xlDytBbjizGUPK3FcGoBPBW4nPSJxquA24CtU7dd2Kdt+1BilBoj97snsPfkNOcxndmnVA5N8ljPPJvEKFXPNjG6rOd61qLJ7dvWoosYQ8nTWpSNMba1c1F721p0dT9K1bPNY9pFLbqoZ5MYperZpt4lciix3bQdYywxutouSuS53nO9dB6LTpvQRnIDcD3wXdKGs9r2EmN0EWMoeVqL8cUYSp7WotsYQ8lzLDGGkqe1GF+MoeRpLbqN0WaMvwQeDfxLjPG+wOHA56duW9enbftQYrQeI4Tw1BDC5aQX358BrgZOqwZo0Kf1/aiL0UWeTWK0HaNEjAbtrfPoohZN7keBWgxiHo4oxlDybB1jLGtnX+Z6H9bOEo9pF7UoMUYX6/NYntdL1GIDxajts4HmepE8GlntXjFPwzsBLwE+DWwF/gJ44GraS4zRRYyh5GktxhdjKHlaC2sx5hhDydNajC/GUPK0FoOsxZfz34uATfn8eavp07Z9KDEK5XkRsCtwQb58KPD2qRgL+xS6H3UxusizSYxWYxSKse717KgWzsORxRhKnoVijGLtbHg/upjrS187Cz2mXdSii+1i3edIoXp38bw+lDVp6TE63C56P9dL5dHk5G9ybQz7AL8fY7xwje0lxugixlDytBbjizGUPK1FtzGGkudYYgwlT2sxvhhDydNadBujxBg3hxB2As4B3hdCuAG4dZV92rYPJUaJMW6JMX43hLAphLApxnh2COH4qRh1fUrcj7oYXeTZJEbbMUrE6KKeXdTCeTi+GEPJs0SMsaydfZnrfVg7SzymXdSii3p2MUeG8rw+lDWpDzGa9Nkoc71UHvXiKveKefLkyZMnT548efLkyZOn9T0Brwb2BLYDjgF+F9h1NX3atg8lRqE8zwB2Av4B+P+ANwFfmIqxsE+h+1EXo4s8m8RoNUahGOtez45q4TwcWYyh5FkoxijWzob3o4u5vvS1s9Bj2kUtutgu1n2OFKp3F8/rQ1mTlh6jw+2i93O9VB5NTv4mlyRJkiT1TwA+QTqk4U7AyTHG766yT9v2ocQoMcZngbsBvwecDlwJPGUqRl2fEvejLkYXeTaJ0XaMEjG6qGcXtXAeji/GUPIsEWMsa2df5nof1s4Sj2kXteiinl3MkaE8rw9lTepDjCZ9NspcL5VHvdXuFfPkyZMnT548efLkyZMnT92cgIOA1wGXAWespU/b9qHEaDMG8BrS76OdA7wMuNeM29b2aXs/6mJ0kWfT27cZo0SMLurZRS2ch+ONMZQ828Rosn2XmgOL2utitG3vaq53kWcXj2kXteiynnW1bFPPEvUutW2u93qx0WKs93YxpLleavtcdPKbXJIkSZLUXzcA1wPfBe65xj5t24cSY81jxBj/Isb4INKL63sDnwkhnFG9YZM+be9HXYwu8lzF7dc8RokYTdrb5tFFLVZxP1vVosMYQ8nTWhSIMZa1sy9zvQ9rZ4nHtItalBiji/V5LM/rJWqxAWPM7bMB53qrPJpwJ5ckSZIk9UwI4SUhhE8DZwK7AS+IMR60mj5t24cSo9QY2ZrfsCiYQ5M81i3PJjEK1nPNMTqu57rVwnk4vhhDybNEjIqxrJ1Lnetd5NnFY1rXXqIWXdSz4znS6+f1oaxJfYjRtE826rm+DnnMtf1qbyBJkiRJWnf7AL8fY7ywRZ+27UOJ0XqMEMJLgCOB3YEPkV6Af3WVfVrfj7oYXeTZJEbbMUrEaNDeOo8uatHkfjTos/Q5NKA8rUXBGGNZO/sy1/uwdpZ4TLuoRYkxulifx/K83qC9xBhjiVHbZwPN9SJ5NBFiOvahJEmSJEkbUghhM3BSzZtltX3WO48u8ixxP0vcjxLa5tFFLaQhG8va2Ze53oe1s8Rjaj3L5tCH53WVNYRts0mfvsx1cCeXJEmSJEmSJEmSBsjf5JIkSZIkSZIkSdLguJNLkiRJkiRJkiRJg+NOLkmSJEmSJEmSJA2OO7kkSZIkSZIkSZI0OP8XmNu3Gxd6Mu0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_k2 = train.drop(['ID_code', 'target'], axis=1).corrwith(train.target).agg('square')\n",
    "\n",
    "f, ax = plt.subplots(figsize=(30,10))\n",
    "target_k2.agg('sqrt').plot.bar(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "k2 = pd.concat([train.drop(['target', 'ID_code'], axis=1), test.drop('ID_code', axis=1)]).corr()**2\n",
    "k2 = np.tril(k2, k=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c88d3bdc18>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20,20))\n",
    "sns.heatmap(np.sqrt(k2), annot=False,cmap='viridis', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_corr = pd.concat([train.drop(['target', 'ID_code'], axis=1), test.drop('ID_code', axis=1)])\n",
    "corr = train_corr.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Absolute Correlations\n",
      "var_92   var_164    0.007601\n",
      "var_81   var_174    0.007408\n",
      "var_122  var_132    0.006868\n",
      "var_171  var_190    0.006533\n",
      "var_1    var_44     0.006506\n",
      "var_148  var_191    0.006477\n",
      "var_53   var_148    0.006432\n",
      "var_81   var_165    0.006398\n",
      "var_36   var_166    0.006395\n",
      "var_18   var_81     0.006394\n",
      "var_75   var_139    0.006204\n",
      "var_71   var_133    0.006181\n",
      "var_115  var_148    0.006162\n",
      "var_110  var_191    0.006098\n",
      "var_53   var_147    0.006062\n",
      "var_166  var_191    0.006032\n",
      "var_2    var_93     0.006027\n",
      "var_180  var_191    0.005978\n",
      "var_34   var_154    0.005977\n",
      "var_67   var_81     0.005931\n",
      "var_117  var_195    0.005908\n",
      "var_146  var_169    0.005907\n",
      "var_3    var_13     0.005904\n",
      "var_26   var_72     0.005899\n",
      "var_133  var_149    0.005868\n",
      "var_146  var_190    0.005836\n",
      "var_115  var_179    0.005766\n",
      "var_133  var_198    0.005758\n",
      "var_31   var_132    0.005758\n",
      "var_22   var_81     0.005745\n",
      "var_53   var_190    0.005707\n",
      "var_94   var_184    0.005700\n",
      "var_14   var_15     0.005696\n",
      "var_2    var_35     0.005696\n",
      "var_1    var_99     0.005689\n",
      "var_20   var_109    0.005684\n",
      "var_64   var_116    0.005657\n",
      "var_102  var_152    0.005653\n",
      "var_92   var_169    0.005640\n",
      "         var_179    0.005613\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def get_redundant_pairs(df):\n",
    "    '''Get diagonal and lower triangular pairs of correlation matrix'''\n",
    "    pairs_to_drop = set()\n",
    "    cols = df.columns\n",
    "    for i in range(0, df.shape[1]):\n",
    "        for j in range(0, i+1):\n",
    "            pairs_to_drop.add((cols[i], cols[j]))\n",
    "    return pairs_to_drop\n",
    "\n",
    "# Function to get top correlations \n",
    "\n",
    "def get_top_abs_correlations(df, n=5):\n",
    "    au_corr = df.corr().abs().unstack()\n",
    "    labels_to_drop = get_redundant_pairs(df)\n",
    "    au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False)\n",
    "    return au_corr[0:n]\n",
    "\n",
    "print(\"Top Absolute Correlations\")\n",
    "print(get_top_abs_correlations(train_corr, 40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_fea = target_k2.loc[np.sqrt(target_k2) > 0.048].index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['var_0', 'var_1', 'var_2', 'var_6', 'var_12', 'var_13', 'var_21',\n",
       "       'var_22', 'var_26', 'var_34', 'var_40', 'var_44', 'var_53', 'var_76',\n",
       "       'var_78', 'var_80', 'var_81', 'var_99', 'var_109', 'var_110', 'var_115',\n",
       "       'var_133', 'var_139', 'var_146', 'var_148', 'var_165', 'var_166',\n",
       "       'var_169', 'var_174', 'var_179', 'var_184', 'var_190', 'var_198'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_fea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polyfeat_train = pd.DataFrame(PolynomialFeatures(2).fit_transform(train[imp_fea]))\n",
    "polyfeat_test = pd.DataFrame(PolynomialFeatures(2).fit_transform(test[imp_fea]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler # this performed better than RandomOverSampler  this is for dealing with highly imbalanced target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import lightgbm as lgb\n",
    "\n",
    "lgbpipe = Pipeline([('resample', RandomOverSampler(random_state=42)), ('model', lgb.LGBMClassifier(random_state=42, objective='binary', metric='auc', \n",
    "                                                                                                   boosting='gbdt', verbosity=1,\n",
    "                                                                                                   tree_learner='serial'))])\n",
    "\n",
    "params = {    \n",
    "    \"model__max_depth\" : [20],\n",
    "    \"model__num_leaves\" : [30],\n",
    "    \"model__learning_rate\" : [0.1],\n",
    "    \"model__subsample_freq\": [5],\n",
    "    \"model__subsample\" : [0.3],\n",
    "    \"model__colsample_bytree\" : [0.05],\n",
    "    \"model__min_child_samples\": [100],\n",
    "    \"model__min_child_weight\": [10],\n",
    "    \"model__reg_alpha\" : [0.12],\n",
    "    \"model__reg_lambda\" : [15.5],\n",
    "    \"model__n_estimators\" : [600]\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('resample', RandomOverSampler(random_state=42, ratio=None, return_indices=False,\n",
       "         sampling_strategy='auto')), ('model', LGBMClassifier(boosting='gbdt', boosting_type='gbdt', class_weight=None,\n",
       "        colsample_bytree=1.0, importance_type='split', learning_rate=0.1,\n",
       "        max_depth...0,\n",
       "        subsample_for_bin=200000, subsample_freq=0, tree_learner='serial',\n",
       "        verbosity=1))]),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'model__max_depth': [20], 'model__num_leaves': [30], 'model__learning_rate': [0.1], 'model__subsample_freq': [5], 'model__subsample': [0.3], 'model__colsample_bytree': [0.05], 'model__min_child_samples': [100], 'model__min_child_weight': [10], 'model__reg_alpha': [0.12], 'model__reg_lambda': [15.5], 'model__n_estimators': [600]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbgrid = GridSearchCV(lgbpipe, param_grid=params, cv=10, scoring='roc_auc')\n",
    "lgbgrid.fit(train.drop(['ID_code', 'target'], axis=1), train.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'model__colsample_bytree': 0.05, 'model__learning_rate': 0.1, 'model__max_depth': 20, 'model__min_child_samples': 100, 'model__min_child_weight': 10, 'model__n_estimators': 600, 'model__num_leaves': 30, 'model__reg_alpha': 0.12, 'model__reg_lambda': 15.5, 'model__subsample': 0.3, 'model__subsample_freq': 5}\n",
      "0.8933538951046179\n"
     ]
    }
   ],
   "source": [
    "print(lgbgrid.best_params_)\n",
    "print(lgbgrid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'model__alpha': 1.0}\n",
      "0.8734118924904035\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeClassifier\n",
    "\n",
    "ridgepipe = Pipeline([('resample', RandomOverSampler(random_state=42)), ('scaler', RobustScaler()), ('model', RidgeClassifier(random_state=42))])\n",
    "\n",
    "params = {'model__alpha': [1.0]} # between 0.5 and 2; best-fit so far: 1\n",
    " \n",
    "ridgegrid = GridSearchCV(ridgepipe, param_grid=params, cv=3, scoring='roc_auc')\n",
    "ridgegrid.fit(pd.concat([train.drop(['ID_code', 'target'], axis=1), polyfeat_train], axis=1, join='inner'), train.target)\n",
    "\n",
    "print(ridgegrid.best_params_)\n",
    "print(ridgegrid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pd.DataFrame(lgbgrid.predict_proba(test.drop(['ID_code'], axis=1))[:, -1], columns=['target'], index=test.loc[:, 'ID_code'])\n",
    "pred.to_csv('submission.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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